Enhanced Survival Prediction in Head and Neck Cancer Using Convolutional Block Attention and Multimodal Data Fusion

• URL: http://arxiv.org/abs/2410.21831v1
• Date: Tue, 29 Oct 2024 07:56:04 GMT
• Title: Enhanced Survival Prediction in Head and Neck Cancer Using Convolutional Block Attention and Multimodal Data Fusion
• Authors: Aiman Farooq, Utkarsh Sharma, Deepak Mishra,
• Abstract summary: This paper proposes a deep learning-based approach to predict survival outcomes in head and neck cancer patients. Our method integrates feature extraction with a Convolutional Block Attention Module (CBAM) and a multi-modal data fusion layer. The final prediction is achieved through a fully parametric discrete-time survival model.
• Score: 7.252280210331731
• License:
• Abstract: Accurate survival prediction in head and neck cancer (HNC) is essential for guiding clinical decision-making and optimizing treatment strategies. Traditional models, such as Cox proportional hazards, have been widely used but are limited in their ability to handle complex multi-modal data. This paper proposes a deep learning-based approach leveraging CT and PET imaging modalities to predict survival outcomes in HNC patients. Our method integrates feature extraction with a Convolutional Block Attention Module (CBAM) and a multi-modal data fusion layer that combines imaging data to generate a compact feature representation. The final prediction is achieved through a fully parametric discrete-time survival model, allowing for flexible hazard functions that overcome the limitations of traditional survival models. We evaluated our approach using the HECKTOR and HEAD-NECK-RADIOMICS- HN1 datasets, demonstrating its superior performance compared to conconventional statistical and machine learning models. The results indicate that our deep learning model significantly improves survival prediction accuracy, offering a robust tool for personalized treatment planning in HNC

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